{"title":"Impact of NEMS Technology over the growth of IoT -A Analysis study","authors":"Kanagaraj Venusamy, M. Tamilselvi","doi":"10.1109/ICECA49313.2020.9297582","DOIUrl":"https://doi.org/10.1109/ICECA49313.2020.9297582","url":null,"abstract":"The semiconductor enterprise will continue to be a critical motive force inside the international prevalent financial system as society turns into an increasing number of relaxation on cell gadgets, the Internet of Things (IoT) emerges, enormous quantities of statistics produced need to be stored and analyzed, and excessive overall performance computing develops to care vital interests in Science, Medicine, Engineering, Technology and Industry. The miniaturized Nano gadgets are the most important element inside the modernized automation which can be speaking with themselves to present answers in a better manner for the technical issues. Thissort of introduced characteristic makes it to create novelty over various fields using downscaling their dimensions near or underneath 1 $mu$ m. The software segmentation for the NEMS marketplace is broadly divided into three groups; Design, Measurement system and solid-state electronics. Each of the packages is divided into level two packages such as microscopy, automotive, clinical, sensors, and memories. Our analysis mainly focuses on the overall performance and functionalities of the NEMS era utilized in the Internet of Things.","PeriodicalId":297285,"journal":{"name":"2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116108247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Manda Thejaswee, P. Srilakshmi, G. Karuna, K. Anuradha
{"title":"Hybrid IG and GA based Feature Selection Approach for Text Categorization","authors":"Manda Thejaswee, P. Srilakshmi, G. Karuna, K. Anuradha","doi":"10.1109/ICECA49313.2020.9297468","DOIUrl":"https://doi.org/10.1109/ICECA49313.2020.9297468","url":null,"abstract":"Feature selection is considered as the most important research area due to its accuracy and time considerations in the field of text classification. If the initial feature set is large, it becomes very important to select the necessary features. Text classification remains as one of the examples that one can see when hundreds or even thousands of records can be included in the size of the feature set. Many research studies are carried out on feature selection by proposing different feature selection approaches for text classification. Although several numbers of studies are done on feature selection, but there is no substantial work to prove the combination of features. The aim of the analysis is to evaluate the redundancy of textual properties selected using a different method such as data set features, algorithms, metrics, a hybrid feature selection method. The test results show that the combination of characteristics chosen by different methods is precise over those selected by each selection process. In any case, the proposed selection of hybrid features depends on the data set characteristics, classification algorithm selection and assessment metrics.","PeriodicalId":297285,"journal":{"name":"2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115471033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Improved Trust Model based on Centrality Measures and Recommendation in Social Network","authors":"Aseel Hussein Zahi, Dr. Saad Talib Hasson","doi":"10.1109/ICECA49313.2020.9297562","DOIUrl":"https://doi.org/10.1109/ICECA49313.2020.9297562","url":null,"abstract":"In this article, a model is developed to improve trust value in the relations that represents social networks by utilizing centrality measures interred with all participants in network-based and recommendations, both on connection or trust. Various central metrics were discussed and implemented to intend to trust. Algorithms are provided to facilitate their calculations. The referral neighbor that has a guaranteed trust boundary is chosen. Trust Value based on Interaction and Recommendations using Centrality Metric (TVIRCM) method is proposed and implemented in this study to improve trust value in the social network when the link between any two nodes represent the unique indication about trust whereas, there are no other standards for maintaining trust. Trust based on interaction refers to trust calculations based on real links observations and exploits the centrality metric. Trust based on recommendation refers to trust calculation based on trust participants of a remote neighbor about other participants.The developed approach is utilized in a trust observation phase as a trust-based interaction (i.e. assigned high and low centrality metrics), then the next phase is based on the proposed recommendation (i.e. the remote neighbor may have certain trust value) and the last phase is the trust calculation phase which based on combining direct and indirect trust.","PeriodicalId":297285,"journal":{"name":"2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124939796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Design and Implementation of Multiple PWM Channels using Universal Asynchronous Receiver Transmitter","authors":"Shikhar","doi":"10.1109/ICECA49313.2020.9297566","DOIUrl":"https://doi.org/10.1109/ICECA49313.2020.9297566","url":null,"abstract":"Universal Asynchronous Receiver Transmitter (UART) is a communication protocol used for sending and receiving the serial data. It offers short distance communication and it is reliable as well. This paper presents the application of UART module for creating Multiple Pulse Width Modulation (PWM) channels having different duty cycles using serial terminal on Field Programmable Gate Arrays (FPGA). The user can control the duty cycle of the PWM signals through serial terminal. UART module designed for this application features technique for baud rate detection. The design has been synthesized using Verilog Hardware Description Language (HDL) on Lattice Mach XO2 FPGA over a Tiny FPGA A2 module using Lattice Diamond Design software. A Printed Circuit Board (PCB) has been designed to observe the effects of PWM signals with different duty cycles over multiple Light Emitting Diodes (LEDs). The design is verified through simulations and logic analyzer tool. Effects of PWM signals is also observed through the intensity of Multiple LEDs. Maximum frequency that can be obtained on Lattice Mach XO2 FPGA is 133 MHz. The design uses 12.08 MHz frequency for the system clock.","PeriodicalId":297285,"journal":{"name":"2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123546416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"AI-Based Techniques for Real-Time Face Recognition-based Attendance System- A comparative Study","authors":"P. Pattnaik, Kalyan Kumar Mohanty","doi":"10.1109/ICECA49313.2020.9297643","DOIUrl":"https://doi.org/10.1109/ICECA49313.2020.9297643","url":null,"abstract":"Face recognition is a powerful tool for a biometric system that takes data from both images and videos. The traditional attendance system can be replaced by the automatic attendance system to utilize class time more effectively. In this paper real-time, attendance monitoring uses a web app that can be operated remotely by using a local server and Amazon Web Service (AWS) cloud recognition Application Programming Interface (API). The first approach follows five sections which are face detection, preprocessing, training and, face recognition through which attendance will be recorded and mailed to the respective teacher. The second approach is based on AWS recognition API which processes the data in the cloud.","PeriodicalId":297285,"journal":{"name":"2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121804702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Performance Analysis of Fuzzy based Restoration Technique for Ink Bleed-through Degraded Documents","authors":"T. Alexander, S.Suresh Kumar, B. Sowmya","doi":"10.1109/ICECA49313.2020.9297394","DOIUrl":"https://doi.org/10.1109/ICECA49313.2020.9297394","url":null,"abstract":"Historical manuscripts are written several hundreds of years ago and are available in the various forms like palm leaves, parchments, wood, stone inscriptions etc. These manuscripts are now undergoing quality degradation due to the factors like physical, weather conditions etc. Digitization is the only way to preserve these manuscripts. Perhaps, in the process of digitalization due to the background colour as well as the other noisy disturbances, the digital image suffers from degradation. Many research works have already been done on the palm leaf manuscripts earlier till now. Here, the proposed research work aims at the restoration of degraded papers in various manuscripts types. In the medieval manuscripts typically written on historical paper, most of them are composed by both sides, as the front and back. Though many specific problems were arising due to the above, bleed-through of the ink from the backside to the front end of the historic document is the one considered for this proposed research. This bleed-through typically performs the possible reading or deciphering the valuable information on those specific pages with considerable difficulty. In existing systems, Independent Component Analysis, Image Enhancement techniques and thresholding methods have been used and typically the results obtained are poor. Hence, the proposed work aims at developing the Ink bleed-through removal from degraded documents employing the fuzzy based technique. The input parameters are preprocessed and then applied to the Fuzzy inference system. At this juncture, the parameters are fuzzified and given to the inference engine with the rule base. The fuzzy output is then defuzzified to obtain the desired output.","PeriodicalId":297285,"journal":{"name":"2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122921980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Safe Prediction of Diabetes Mellitus Using Weighted Conglomeration of Mining Schemes","authors":"Shiva Shankar Reddy, Nilambar Sethi, R. Rajender","doi":"10.1109/ICECA49313.2020.9297390","DOIUrl":"https://doi.org/10.1109/ICECA49313.2020.9297390","url":null,"abstract":"Diabetes mellitus (DM) is a chronic disease, from which most people are suffering in the present day. In this work, an attempt has been made to propose an ensemble of numerous techniques for the diagnosis and analysis of diabetic Mellitus data. The prime focus has been given to a safe prediction. One of the reasons for focusing on this ailment is due to its increasing rate of co-existence and occurrence over the world. This ailment is causing approximately one and a half million casualties every year. This work is meant to mitigate the challenge of early prediction of DM. For this, four different data mining techniques have been utilized namely lazy K-star, multi-layer perceptron (MLP), logistic regression and random forest. Using these four techniques a conglomerate algorithm was proposed which gives the final predicted label. If a patient test data is assigned a positive DM label by more than three classifiers then only it is assigned with the final label as positive DM, otherwise, it is treated as a negative. Satisfactory results in terms of the overall rate of accuracy have been obtained through this ensemble approach. Here, accuracy refers to the correct classification and prediction of DM through the proposed scheme. The proposed algorithm obtained an overall accuracy of 98.25%.","PeriodicalId":297285,"journal":{"name":"2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123875076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Control of Standalone DFIG based Wind Turbine Generator using Machine Learning Algorithm","authors":"R. Mahalakshmi, K. Reddy, M. Gautam","doi":"10.1109/ICECA49313.2020.9297603","DOIUrl":"https://doi.org/10.1109/ICECA49313.2020.9297603","url":null,"abstract":"Electrical energy extraction from non-conventional energy sources such as solar, wind, etc., is very essential nowadays due to the huge electricity demand. The integration of these sources into the grid/electrical loads face many technical challenges like grid synchronization, power oscillations, etc., The modern wind power plants use Doubly Fed Induction Generator (DFIG) based WTGs as it has embedded Rotor Side Converter (RSC) and Stator Side Converter (SSC). This paper focuses on the performance analysis of standalone Doubly Fed Induction Generator (DFIG) based Wind Turbine using a new control strategy at RSC side. The RSC control is developed with the use of a linear regression algorithm under the Machine Learning (ML) technique. The effectiveness of the controller is validated using MATLAB/Simulink for the different operating conditions such as varying wind speed and load variations etc., The experimental setup of RSC is implemented in hardware and the results are discussed.","PeriodicalId":297285,"journal":{"name":"2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124180652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
V. Kaleeswaran, S. Dhamodharavadhani, R. Rathipriya
{"title":"A Comparative Study of Activation Functions and Training Algorithm of NAR Neural Network for Crop Prediction","authors":"V. Kaleeswaran, S. Dhamodharavadhani, R. Rathipriya","doi":"10.1109/ICECA49313.2020.9297469","DOIUrl":"https://doi.org/10.1109/ICECA49313.2020.9297469","url":null,"abstract":"The proposed study in this paper provides long-term crop prediction for Tamilnadu, India. Nonlinear Autoregressive (NAR) Neural Network (NN) with different parameter settings has been used to facilitate the correct quality and quantity of crop production. At the core of this study is to compare the effect of training algorithms (such as trainlm, trainbr, trainscg, traincgf, trainbfg, traincgf) and activation functions (such as tansig, elliotsig, logsig and purelin) in the performance of the crop yield forecasting model. This study showed that activation functions elliotsig and tansig with the training algorithm trainbr of NARNN delivered the most promising results based on the smallest error between actual and predicted value compared to the other activation and training functions of NARNN.","PeriodicalId":297285,"journal":{"name":"2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129323605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Artificial Bee Colony assisted Spectrum Sharing Scheme for NOMA based Cognitive Radio Networks","authors":"K. Sultan","doi":"10.1109/ICECA49313.2020.9297496","DOIUrl":"https://doi.org/10.1109/ICECA49313.2020.9297496","url":null,"abstract":"In this paper, a cooperative spectrum sharing scheme is proposed for NOMA based cognitive radio networks comprising of primary and secondary networks. The primary network consists of a primary transmitter PT communicating with a primary receiver PU, whereas the secondary network consists of a NOMA based secondary transmitter ST communicating with L secondary users SUs. The primary terminals are separated far apart therefore ST provides assistance as a relay in order to enable their end-to-end communication. Each terminal is equipped with a single antenna therefore end-to-end communication is accomplished in two time-slots. In first timeslot, PT transmits its signal to ST and at the same time one best SU retransmits the primary signal of the last frame to PU. In second time-slot, ST transmits the superimposed signals of primary and secondary networks. In this scenario, sum rate of SUs is maximized while ensuring to guarantee the QoS of PU. Artificial Bee Colony (ABC) global optimization algorithm is employed to solve this transmit power allocation problem which quickly converged to the best solution.","PeriodicalId":297285,"journal":{"name":"2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)","volume":"127 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128024353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}